Structural production layer decomposition: a new method to measure differences between MRIO databases for footprint assessments

Recent empirical assessments revealed that footprint indicators calculated with various multi-regional input–output (MRIO) databases deliver deviating results. In this paper, we propose a new method, called structural production layer decomposition (SPLD), which complements existing structural decomposition approaches. SPLD enables differentiating between effects stemming from specific parts in the technology matrix, e.g. trade blocks vs. domestic blocks, while still allowing to link the various effects to the total region footprint. Using the carbon footprint of the EU-28 in 2011 as an example, we analyse the differences between EXIOBASE, Eora, GTAP and WIOD. Identical environmental data are used across all MRIO databases. In all model comparisons, variations in domestic blocks have a more significant impact on the carbon footprint than variations in trade blocks. The results provide a wealth of information for MRIO developers and are relevant for policy makers designing climate policy measures targeted to specific stages along product supply chains.     

UNCERTAINTY ANALYSIS FOR MULTI-REGION INPUT–OUTPUT MODELS – A CASE STUDY OF THE UK'S CARBON FOOTPRINT

This paper reviews and demonstrates methods available for estimating standard deviations for carbon multipliers in a multi-regional input–output (MRIO) framework. We attempt to capture all possible variations of underlying data and calculation procedures in a global MRIO model constructed with particular focus on the UK. We consider these variations to be random, and determine the stochastic variation of the whole MRIO system using Monte Carlo techniques. 5000 simulation runs were carried out to determine the standard deviations of multipliers. From these, the standard deviations of components of the UK's carbon footprint were estimated using error propagation. We estimate an 89% probability that the UK's carbon footprint has increased between 1994 and 2004.     

A METHOD TO CREATE CARBON FOOTPRINT ESTIMATES CONSISTENT WITH NATIONAL ACCOUNTS

Although multiregional input–output (MRIO) databases use data from national statistical offices, the reconciliation of various data sources results in significantly altered country data. This makes it problematic to use MRIO-based footprints for national policy-making. This paper develops a potential solution using the Netherlands as case study. The method ensures that the footprint is derived from an MRIO dataset (in our case the World Input–Output Database (WIOD)) that is made consistent with Dutch National accounts data. Furthermore, usage of microdata allows us to separate re-exports at the company level. The adjustment results in a foreign footprint in 2009 that is 22% lower than the original WIOD estimates and a significantly altered country allocation. We demonstrate that already in the data preparation phase due to the treatment of re-exports and margins, large differences arise with Dutch national statistics, which may help explain the variation in footprint estimates across MRIO databases.     

CONVERGENCE BETWEEN THE EORA,WIOD, EXIOBASE,AND OPENEU'S CONSUMPTION-BASED CARBON ACCOUNTS

In this paper, we take an overview of several of the biggest independently constructed global multi-regional input–output (MRIO) databases and ask how reliable and consonant these databases are. The key question is whether MRIO accounts are robust enough for setting environmental policies. This paper compares the results of four global MRIOs: Eora, WIOD, EXIOBASE, and the GTAP-based OpenEU databases, and investigates how much each diverges from the multi-model mean. We also use Monte Carlo analysis to conduct sensitivity analysis of the robustness of each accounts’ results and we test to see how much variation in the environmental satellite account, rather than the economic structure itself, causes divergence in results. After harmonising the satellite account, we found that carbon footprint results for most major economies disagree by<10% between MRIOs. Confidence estimates are necessary if MRIO methods and consumption-based accounting are to be used in environmental policy-making at the national level.     

Accounting for value added embodied in trade and consumption: an intercomparison of global multiregional input–output databases

Global multiregional input–output (MRIO) tables constitute detailed accounts of the economic activity worldwide. Global trade models based on MRIO tables are being used to calculate important economic and environmental indicators such as value added in trade or the carbon footprint of nations. Such applications are highly relevant in international trade and climate policy negotiations, and consequently MRIO model results are being scrutinized for their accuracy and reproducibility. We investigate the variation in results from three major MRIO databases by comparing underlying economic data and territorial and consumption-based results across databases. Although global value-added accounts were similar across databases, we find some significant differences at the level of individual countries and sectors. Model disagreement was relatively stable from the territorial to the consumption perspective. Pairwise matrix comparison statistics indicated that the Global Trade Analysis Project and World Input-Output Database MRIO tables were overall more similar to each other than either was to the Eora database.     

HARMONISING NATIONAL INPUT—OUTPUT TABLES FOR CONSUMPTION-BASED ACCOUNTING — EXPERIENCES FROM EXIOPOL

Environmentally extended, multi-regional, input–output (MRIO) databases have emerged to fulfil the need for mapping the impacts of globalisation, following resource-intensive supply chains crossing country borders. EXIOBASE is one such data set designed for use in analysis relevant to resource use and European Union policy. It provides the most detailed harmonised sector classification in any MRIO and integrates data from a wide range of sources. We review the necessary steps in order to harmonise source data in MRIO databases, and describe methods to increase the product and industry detail of aggregate supply and use tables (SUTs) in order to provide a homogenous classification across countries that allows resource-specific modelling. We cover mathematical programming approaches used to reconcile data sets, and investigate some implications of reverse engineering symmetric input–output tables and disaggregating the SUTs. We focus particularly on the footprint multiplier at the product level, where policy formation is targeted.     

A CARBON FOOTPRINT TIME SERIES OF THE UK – RESULTS FROM A MULTI-REGION INPUT–OUTPUT MODEL

The framework and results of an international multi-region input–output (MRIO) model for the UK are presented. A time series of balanced input–output tables for the UK was constructed for the period 1992 to 2004 by using a matrix balancing procedure that is able to handle conflicting external data and inconsistent constraints. Detailed sectoral and country-specific trade data for the UK were compiled and reconciled with the UK input–output data, and economic and environmental accounts for three world regions were integrated in a UK-specific MRIO model. This was subsequently used to calculate a time series of national carbon footprints for the UK from 1992 to 2004. Greenhouse gas emissions embedded in UK trade are distinguished by destination of imports to intermediate and final demand. Most greenhouse gases show a significant increase over time in consumer emissions and a widening gap between producer and consumer emissions. Net CO₂ emissions embedded in UK imports increased from 4.3% of producer emissions in 1992 to a maximum of 20% in 2002. The total estimated UK carbon footprint in 2004 was 730 Mt for CO₂ and 934 Mt CO₂ equivalents for all greenhouse gases.     

EFFECTS OF SECTOR AGGREGATION ON CO2 MULTIPLIERS IN MULTIREGIONAL INPUT–OUTPUT ANALYSES

The past few years have seen the emergence of several global multiregional input–output (MRIO) databases. Due to the cost and complexity of developing such extensive tables, industry sectors are generally represented at a rather aggregate level. Currently, one of the most important applications of input–output analysis is environmental assessments, for which highly aggregate sectors may not be sufficient to yield accurate results. We experiment with four of the most important global MRIO systems available, analyzing the sensitivity of a set of aggregate CO₂ multipliers to aggregations in the MRIO tables used to calculate them. Across databases, we find (a) significant sensitivity to background system detail and (b) that sub-sectors contained within the same aggregate MRIO sector may exhibit highly different carbon multipliers. We conclude that the additional information provided by the extra sector detail may warrant the additional costs of compilation, due to the heterogeneous nature of economic sectors in terms of their environmental characteristics.     

REGIONAL WATER FOOTPRINTS OF THE YANGTZE RIVER: AN INTERREGIONAL INPUT–OUTPUT APPROACH

Recently, researchers have applied the multi-regional input–output (MRIO) approach to water footprint (WF) analysis. The concept of interregional input–output (R-MRIO) was developed to analyse regional issues. Researchers have concentrated on the development of global or international input–output (N-MRIO) tables. Using the N-MRIO and the R-MRIO approach allows the study of global and regional issues, respectively. The WF is an indicator influenced by trade among nations and regions. However, the treatment of imports in an R-MRIO approach differs in whether international imports are separated or combined. We evaluate the effects of the difference between these models and discuss policy implications for the Yangtze River, China. The WF calculated using the combined type model is 11% larger than that by the separated type model. This difference can be ascribed to international imports, mainly internal consumption and interregional trade. We find that this difference affects social equity in water-abundant areas.     

THE ‘REST OF THE WORLD’ – ESTIMATING THE ECONOMIC STRUCTURE OF MISSING REGIONS IN GLOBAL MULTI-REGIONAL INPUT–OUTPUT TABLES

Incomplete data for the economic structure of numerous countries hamper the compilation of global multi-regional input–output (MRIO) tables. By themselves, most of these countries are of only limited importance for the global economy and incumbent environmental issues. Hence, in most recent global MRIO tables these countries are either roughly estimated or summarised in one rest of the world (RoW) region. Combining a wide range of countries, this RoW region may play a significant role in global economic and environmental accounts. We conceptualise the importance of RoW in several environmental footprint accounts and present algorithms to estimate the structure of RoW. The approach utilises the information of the economic structure within known parts of the MRIO table to estimate the unknown structure. Using this method, global warming potential and employment footprints remain stable irrespective of the chosen initial estimates, whereas natural land use footprints and individual product impacts vary significantly.     

A STRUCTURAL DECOMPOSITION APPROACH TO COMPARING MRIO DATABASES

The construction of multi-regional input–output tables is complex, and databases produced using different approaches lead to different analytical outcomes. We outline a decomposition methodology for investigating the variations that exist when using different multiregional input–output (MRIO) systems to calculate a region's consumption-based account. Structural decomposition analysis attributes the change in emissions to a set of dependent determinants, such as technical coefficients, the Leontief inverse and final demands. We apply our methodology to three MRIO databases – Eora, GTAP and WIOD. Findings reveal that the variation between Eora and GTAP can be attributed to differences in the Leontief inverse and emissions’ data, whereas the variation between Eora and WIOD is due to differences in final demand and the Leontief inverse. For the majority of regions, GTAP and WIOD produce similar results. The approach in this study could help move MRIO databases from the academic arena to a useful policy instrument.     

PAS2050规范下物流服务碳足迹研究

文中在对国内外关于物流与供应链碳足迹研究现状作了一定综述评论,在此基础上提出物流服务碳足迹概念,利用PAS2050规范构建针对第三方物流服务公司分析物流业务碳足迹模型和测算方法,为物流企业清楚地认识在物流服务过程中碳足迹分解提供了一种分析方法,为企业进行低碳物流运作升级提供了理论依据和实践指导。     

2001-2011年江西省生态足迹分析及预测

本文利用生态足迹模型、相关分析以及灰色预测模型对江西省的生态足迹进行分析。结果表明:2011年江西省的人均生态足迹比2001年增加了68.05%,为2.2366hm2/人;生态足迹与GDP、城镇化、居民消费水平及人口的相关系数均在0.94以上;预测得2012年江西人均GDP生态足迹为3659.247hm2,2019年为1798.4273hm2,即未来江西发展的不可持续状态将逐步得到改善。     

How a Firm's Domestic Footprint and Domestic Environmental Uncertainties Jointly Shape Added Cultural Distances: The Roles of Resource Dependence and Headquarters Attention

Even though many firms conduct most of their business domestically, international management research has remained remarkably silent on the role of a firm's domestic footprint in its internationalization strategy. We shed light on that role by exploring how the size of a firm's domestic footprint influences the cultural distance that the firm adds to its country portfolio when expanding internationally. Integrating resource dependence theory and the attention‐based view, we hypothesize that a firm's domestic footprint has a negative relationship with added cultural distance (ACD), and that domestic policy uncertainty strengthens this relationship whereas domestic demand uncertainty weakens it. We find robust support for our hypotheses in a sample of the world's largest retailers covering the period 2000–07, indicating that a firm's domestic footprint and domestic environmental uncertainties jointly shape cross‐cultural expansion strategies. Our findings suggest that ACDs reflect headquarters executives' desire to avoid ineffective foreign expansions, hinting at possible biases in studies of the performance effects of distance.     

REGIONAL AND SECTORAL DISAGGREGATION OF MULTI-REGIONAL INPUT–OUTPUT TABLES – A FLEXIBLE ALGORITHM

A common shortcoming of available multi-regional input–output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.     

Emissions burden shifting in global value chains – winners and losers under multi-regional versus bilateral accounting

ABSTRACT

International trade leads to emissions burden shifting and threatens mitigation targets. Multiregional input–output (MRIO) and bilateral trade input–output (BTIO) models are widely used to analyse emissions embodied in trade and global value chains. Especially, the last one is used in analysing border tax adjustment (BTA) on the carbon content of imports. The model choice is not trivial. The analysis shows BTIO's inability to capture the consumer-principle throughout the production chain and its inadequacy as an option for consumption-based accounting, because it allocates emissions to the first importing country and to the sector of production, instead to the consumer (both country and region). Regarding the BTA assessment, BTIO tax domestic carbon content of direct imports, but not indirect imported carbon content. MRIO does provide incentives for mitigation in third countries. The differences in allocation of emissions and taxes’ burden of both models have different consequences for developed and undeveloped regions.     

How to prove,how to interpret and what to do? Uncertainty experiences of street-level tax officials

This study examines the kind of uncertainties frontline tax officials working with a trust-based inspection approach experience in interacting with citizen-clients. The classical literature on bureaucracy and the street-level bureaucracy literature suggest frontline officials face two kinds of uncertainties: information and interpretation problems. Analysing stories of Dutch frontline tax officials collected through in-depth interviews, this article shows that these two kinds of uncertainty only explain a part of the uncertainties experienced. Respondents also face action problems requiring improvisational judgements. The study furthermore finds that different sources underlie these uncertainties, pointing to possible explanations.     

量块定位方式对坐标测量机精度的影响

量块的定位方式对校准坐标测量机的尺寸测量参数有着重要影响。本文首先分析了标准量块的两种定位方式对校准精度的影响。在实际校准工作中,以不同尺寸的标准量块采取不同的定位方式来减少定位误差提高校准精度,并进行不确定度分析。     

CONSTRUCTING AN ENVIRONMENTALLY-EXTENDED MULTI-REGIONAL INPUT–OUTPUT TABLE USING THE GTAP DATABASE

The use of Multi-Regional Input–Output Analysis (MRIOA) for understanding global environmental problems is growing rapidly. Renewed interest in MRIOA has led to several large research projects focused on constructing detailed and accurate MRIOTs. However, very few researchers have made use of the already available and regularly updated database produced by the Global Trade Analysis Project (GTAP). We demonstrate and discuss how the GTAP database can be converted into an MRIOT without the need for additional balancing. An illustrative example uses the GTAP-MRIO to reallocate carbon dioxide emissions from producing to consuming countries. We suggest that an MRIOT that treats international transport exogenously is adequate until more reliable data on international transport margins and emissions are available. To focus resources and refine methods, a concerted research effort is needed to compare the results of the GTAP-MRIO model with the new MRIO datasets under development.     

Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis

Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.